Plotting all marker features

workflowr

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Last updated: 2025-10-16

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Knit directory: 13384_GBMHGG_SPP1_Xenium/

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/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/colors_themes.R	code/colors_themes.R
/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/plot_functions.R	code/plot_functions.R

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Import packages

suppressPackageStartupMessages({
  library(workflowr)
  library(arrow)
  library(Seurat)
  library(SeuratObject)
  library(SeuratDisk)
  library(tidyverse)
  library(tibble)
  library(ggplot2)
  library(ggpubr)
  library(ggrepel)
  library(googlesheets4)
  library(workflowr)})

Environment variables and helper functions

setwd("/home/hnatri/13384_GBMHGG_SPP1_Xenium/")
set.seed(9999)
options(scipen = 99999)
options(ggrepel.max.overlaps = Inf)

#source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/colors_themes.R")
source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/plot_functions.R")

# Cluster colors
main_clusters <- as.factor(c(0, seq(1:20)))
main_cluster_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(main_clusters))
names(main_cluster_col) <- levels(main_clusters)

Import data

# Copied to isilon /tgen_labs/banovich/PIPAC/Seurat
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/13384_GBMHGG_Xenium/Seurat/spatial_clustered_NN30_PC50_Seurat.rds")

seurat_data <- NormalizeData(seurat_data)
VariableFeatures(seurat_data) <- rownames(seurat_data)
seurat_data <- ScaleData(seurat_data)

DimPlot(seurat_data,
        reduction = "sp",
        group.by = "leiden_1.0",
        cols = main_cluster_col,
        raster = T,
        label = F) +
    coord_fixed(ratio = 1) +
    theme_minimal() +
    NoLegend() +
    ggtitle("TMA1_0069754")

Feature annotations

gs4_deauth()
all_sheets  <- gs4_get("https://docs.google.com/spreadsheets/d/1V1YxYPMI-J1ilZ3ooTDM323s5VMIf7E9f5XsuIOaGAg/edit?usp=sharing")
sheet_names(all_sheets)

[1] "Metadata"                          "Main cluster annotations"         
[3] "Immune annotations"                "Immune top markers"               
[5] "Non-immune annotations"            "Non-immune top markers"           
[7] "Multi-tissue and cancer basepanel" "Custom probes"                    

multissue_cancer <- read_sheet(all_sheets, sheet = "Multi-tissue and cancer basepanel")
custom_panel <- read_sheet(all_sheets, sheet = "Custom probes")

Annotation groups

#unique(multissue_cancer$Annotation)

fibroblast_markers <- multissue_cancer[grep("Fibroblast", multissue_cancer$Annotation),]$Gene # 65

immune_markers <- multissue_cancer[grep("Immune", multissue_cancer$Annotation),]$Gene # 6

macrophage_markers <- multissue_cancer[grep("Macrophage", multissue_cancer$Annotation),]$Gene # 70
granulocyte_markers <- multissue_cancer[grep("Granulocytes", multissue_cancer$Annotation),]$Gene # 25
dendritic_markers <- c(multissue_cancer[grep("dendritic", multissue_cancer$Annotation),]$Gene,
                       multissue_cancer[grep("Dendritic", multissue_cancer$Annotation),]$Gene) #22

tcell_markers <- multissue_cancer[grep("T cells", multissue_cancer$Annotation),]$Gene # 62
bcell_markers <- multissue_cancer[grep("B cells", multissue_cancer$Annotation),]$Gene # 45

proliferation_markers <- multissue_cancer[grep("Proliferation", multissue_cancer$Annotation),]$Gene # 7
endothelial_markers <- multissue_cancer[grep("Endothelial", multissue_cancer$Annotation),]$Gene # 82

Spatial plots of all base-panel genes

all_genes <- c(custom_panel$Gene) #multissue_cancer$Gene
all_genes <- gsub("CD11b", "ITGAM", all_genes)
all_genes <- gsub("CD11c", "ITGAX", all_genes)
all_genes <- gsub("CD31", "PECAM1", all_genes)
all_genes <- gsub("CD45", "PTPRC", all_genes)
all_genes <- gsub("HLA-DR", "HLA-DRA", all_genes)
all_genes <- gsub("HLA.DPB1", "HLA-DPB1", all_genes)
all_genes <- gsub("IFNGR", "IFNGR1", all_genes)
all_genes <- gsub("TGFβ", "TGFB1", all_genes)
all_genes <- gsub("TGFβR", "TGFB1R", all_genes)
all_genes <- gsub("TGFB1R", "TGFBR1", all_genes)
all_genes <- gsub("IL-1R", "IL1R2", all_genes)

all_genes <- sort(all_genes)

#setdiff(all_genes, rownames(seurat_data))
#grep("TGFB", rownames(seurat_data), value = T)

for(gene in all_genes){
  message(gene)
  
  print(FeaturePlot(seurat_data,
              slot = "data",
              features = gene,
              order = T,
              reduction = "sp",
              raster = T,
              ncol = 1,
              cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend())
}

ALDOC

APOE

BMPR1B

BNIP3L

C1QA

C1QB

C1QC

CAMK2B

CCL2

CCL4

CD44

CD47

CD63

CD72

CD74

CD80

CDH2

CDK4

CDKN2B

CEMIP2

COL1A1

CTSB

CX3CR1

CXCL1

CXCL12

CXCL8

ENO1

FCGR3A

FGFR1

FGFR2

FLT1

FN1

GABRD

GAPDH

GFAP

HIF1A

HK2

HLA-DPB1

HLA-DRA

HMOX1

HOMER1

ICAM1

IFI6

IFNG

IFNGR1

IGFBP2

IGFBP3

IL13RA2

IL1A

IL1B

IL1R2

ISG15

ITGA3

ITGA4

ITGA5

ITGAE

ITGAM

ITGAX

ITGB1

JAK1

LDHA

LYZ

MADCAM1

MARCO

MIF

MX1

NF1

NLRP3

NR4A3

NRP1

OLIG1

OLIG2

OPALIN

OSM

P2RY12

P2RY13

P4HB

PDK4

PECAM1

PGK1

PTPRC

PTPRD

RHOB

RNASE1

S100A8

S100A9

SLC2A1

SPP1

STAT3

STAT4

SYN1

TERT

TGFB1

TGFBI

TGFBR1

TIMP1

TMEM119

TOX

TP53

TYROBP

VCAM1

VEGFA

VEGFB

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.18.0 viridis_0.6.3         viridisLite_0.4.2    
 [4] RColorBrewer_1.1-3    googlesheets4_1.1.0   ggrepel_0.9.3        
 [7] ggpubr_0.6.0          lubridate_1.9.2       forcats_1.0.0        
[10] stringr_1.5.0         dplyr_1.1.2           purrr_1.0.1          
[13] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[16] ggplot2_3.4.2         tidyverse_2.0.0       SeuratDisk_0.0.0.9021
[19] Seurat_5.0.1          SeuratObject_5.0.1    sp_1.6-1             
[22] arrow_21.0.0.1        workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.20       splines_4.3.0          later_1.3.1           
  [4] cellranger_1.1.0       polyclip_1.10-4        fastDummies_1.7.3     
  [7] lifecycle_1.0.3        rstatix_0.7.2          doParallel_1.0.17     
 [10] rprojroot_2.0.3        globals_0.16.2         processx_3.8.1        
 [13] lattice_0.21-8         hdf5r_1.3.8            MASS_7.3-60           
 [16] backports_1.4.1        magrittr_2.0.3         plotly_4.10.2         
 [19] sass_0.4.6             rmarkdown_2.22         jquerylib_0.1.4       
 [22] yaml_2.3.7             httpuv_1.6.11          sctransform_0.4.1     
 [25] spam_2.9-1             spatstat.sparse_3.0-1  reticulate_1.29       
 [28] cowplot_1.1.1          pbapply_1.7-0          abind_1.4-5           
 [31] Rtsne_0.16             BiocGenerics_0.48.1    git2r_0.32.0          
 [34] circlize_0.4.15        S4Vectors_0.40.2       IRanges_2.36.0        
 [37] irlba_2.3.5.1          listenv_0.9.0          spatstat.utils_3.0-3  
 [40] goftest_1.2-3          RSpectra_0.16-1        spatstat.random_3.1-5 
 [43] fitdistrplus_1.1-11    parallelly_1.36.0      leiden_0.4.3          
 [46] codetools_0.2-19       shape_1.4.6            tidyselect_1.2.0      
 [49] farver_2.1.1           stats4_4.3.0           matrixStats_1.0.0     
 [52] spatstat.explore_3.2-1 googledrive_2.1.0      jsonlite_1.8.5        
 [55] GetoptLong_1.0.5       ellipsis_0.3.2         progressr_0.13.0      
 [58] ggridges_0.5.4         survival_3.5-5         iterators_1.0.14      
 [61] foreach_1.5.2          tools_4.3.0            ica_1.0-3             
 [64] Rcpp_1.0.10            glue_1.6.2             gridExtra_2.3         
 [67] xfun_0.39              withr_2.5.0            fastmap_1.1.1         
 [70] fansi_1.0.4            callr_3.7.3            digest_0.6.31         
 [73] timechange_0.2.0       R6_2.5.1               mime_0.12             
 [76] colorspace_2.1-0       scattermore_1.2        tensor_1.5            
 [79] spatstat.data_3.0-1    utf8_1.2.3             generics_0.1.3        
 [82] data.table_1.14.8      httr_1.4.6             htmlwidgets_1.6.2     
 [85] whisker_0.4.1          uwot_0.1.14            pkgconfig_2.0.3       
 [88] gtable_0.3.3           lmtest_0.9-40          htmltools_0.5.5       
 [91] carData_3.0-5          dotCall64_1.0-2        clue_0.3-64           
 [94] scales_1.2.1           png_0.1-8              knitr_1.43            
 [97] rstudioapi_0.14        rjson_0.2.21           tzdb_0.4.0            
[100] reshape2_1.4.4         curl_5.0.0             nlme_3.1-162          
[103] GlobalOptions_0.1.2    cachem_1.0.8           zoo_1.8-12            
[106] KernSmooth_2.23-21     parallel_4.3.0         miniUI_0.1.1.1        
[109] pillar_1.9.0           vctrs_0.6.2            RANN_2.6.1            
[112] promises_1.2.0.1       car_3.1-2              xtable_1.8-4          
[115] cluster_2.1.4          evaluate_0.21          cli_3.6.1             
[118] compiler_4.3.0         rlang_1.1.1            crayon_1.5.2          
[121] future.apply_1.11.0    ggsignif_0.6.4         labeling_0.4.2        
[124] ps_1.7.5               getPass_0.2-4          plyr_1.8.8            
[127] fs_1.6.2               stringi_1.7.12         deldir_1.0-9          
[130] assertthat_0.2.1       munsell_0.5.0          lazyeval_0.2.2        
[133] spatstat.geom_3.2-1    Matrix_1.6-5           RcppHNSW_0.5.0        
[136] hms_1.1.3              patchwork_1.1.2        bit64_4.0.5           
[139] future_1.32.0          shiny_1.7.4            highr_0.10            
[142] ROCR_1.0-11            gargle_1.4.0           igraph_1.4.3          
[145] broom_1.0.4            bslib_0.4.2            bit_4.0.5